Package 'GenomicInteractions'

Description

Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics.

Description

This function reads in the interaction data processed by [hiclib](https://bitbucket.org/mirnylab/hiclib). The data is extracted from hiclib and exported from its internal HDF5 format to text using h5dictToTxt.py. This function reads in the relevant files present in the directory and generates a data.table containing all of the information. It assumes that there are files with the following names in the directory: fragids1, chrms1, mids1, fraglens1, fragids2, chrms2, mids2, fraglens2, distances.

Usage

.importHicLib(dir, chr_names)

Arguments

Value

data.table containing information on the individual interactions

Description

This function reads in the interaction data outputted by HOMER (http://homer.salk.edu/homer/interactions/).

Usage

.importHomer(fn)

Arguments

Value

a data frame containing the relevant information

Description

In bed(n) formats interaction data cannot be stored directly due to the inability of the format to handle trans-interactions. It is only capable of storing cis-interactions through the use of blockStarts and blockEnds. As such the information is typically stored as a string of the format chr1:start1..end1-chr2:start2..end2,N (e.g. \'chr1:3268539..3269061-chr10:103124111..103124631,4\'.). This function takes a vector of these strings and converts them to a data.frame.

Usage

.processChiapetName(x)

Arguments

Value

a data.frame containing all of the processed information

Description

Reads in interactions stored in a BAM file. Assumes that each interaction is represented by pair of PETs with the same qname. The function reads in and determines which anchor is which by examining the isFirstMateRead and isSecondMateRead fields in the BAM file.

Usage

.readBam(fn)

Arguments

Value

list of GRanges - storing the anchor information for each interaction

Description

Reads in interactions stored in a a pair of BAM files, e.g. from independent alignment of paired-end reads. Assumes that each interaction is represented by pair of PETs with the same qname (this may not always be true. Depending on data origin read qnames may end in '/1' or '/2' to denote first or second read in the pair). The function reads in files, removes unpaired reads, and pairs reads based on macthing qnames.

Usage

.readTwoBams(fn)

Arguments

Value

list of two GRanges, storing the anchor information for each interaction

Description

Check for columns in a data.frame.

Usage

.validateInput(x, h)

Arguments

Value

list of two GRanges, storing the anchor information for each interaction

Description

This function annotates the regions of a GInteractions object according to their overlaps with 'annotations', a list of named GRanges (or GRangesList) objects.

Usage

annotateInteractions(GIObject, annotations, id.col = NULL)

## S4 method for signature 'GInteractions,list'
annotateInteractions(GIObject, annotations, id.col = NULL)

Arguments

Details

Metadata columns will be added to the regions of the GInteractions object, named according to the names of 'annotations' and containing the id(s) of the corresponding overlapping genomic interval(s). If 'annotations' is not named, the metadata columns will be named 'FEATURE#.id' where # is the position in the list.

IDs for features in each element of annotations will be extracted according to the following rules: if the annotation features are a GRanges object and a metadata column is specified using 'id.col', this column will be used as feature IDs. Otherwise, if the annotation features are named, their names will be used. If neither of these are present, they will be given numeric IDs.

For each anchor a 'node.class' metadata column will also be added, containing the name of the list element which was first annotated to each range. Ranges with no overlaps will be classified as 'distal'.

Value

invisible(1)

Examples

library('GenomicRanges')
data(hic_example_data)
data(mm9_refseq_promoters)
mm9_refseq_grl = split(mm9_refseq_promoters, mm9_refseq_promoters$id)
# This adds a `promoter.id` metadata column to regions(hic_example_data)
# containing IDs of overlapping promoters for each region, taken from
# names(mm9_refseq_grl)
annotateInteractions(hic_example_data, list(promoter=mm9_refseq_grl))

Description

Use this function to add metadata parallel to the 'regions' slot of a GenomicInteractions or GInteractions object.

Usage

annotateRegions(GIObject, name, dat)

## S4 method for signature 'GInteractions,character,vector'
annotateRegions(GIObject, name, dat)

Arguments

Value

invisible(1)

Examples

data(hic_example_data)
chip <- runif(n = length(regions(hic_example_data)), max = 1000)
annotateRegions(hic_example_data, 'chip', chip)

Description

Coerce the structure of an object to one following BED-like conventions, i.e., with columns for blocks and thick regions.

Usage

## S4 method for signature 'GInteractions'
asBED(x, keep.mcols = FALSE, score = "score", ...)

Arguments

Value

A 'GRanges', with the metadata columns 'name', 'blockStarts' and 'blockSizes' added.

Examples

data(hic_example_data)
asBED(hic_example_data)

Description

The default display parameters for a track object class can be queries using the availableDisplayPars function.

Usage

availableDisplayPars(class)

Arguments

Value

returns a list of the default display parameters.

Examples

availableDisplayPars('InteractionTrack')

Description

This function takes a GInteractions object and calculates the distances between the anchors according to the value of method. The distances returned follow the same convention as distance(x, y) in GenomicRanges where the distance between adjacent regions is 0. Note that if anchors are overlapping this method will print a warning and return the distance as 0.

Usage

calculateDistances(GIObject, method = "midpoint", floor = TRUE)

## S4 method for signature 'GInteractions'
calculateDistances(GIObject, method = "midpoint", floor = TRUE)

Arguments

Value

An vector containing the distances between anchors/GRanges, NA if on different chromosomes, rounded down to the nearest bp.

Examples

library(GenomicRanges)

anchor.one <- GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), 
  IRanges(c(10, 20, 30, 20), width = 5))
anchor.two <- GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), 
  IRanges(c(100, 200, 300, 50), width = 5))
interaction_counts <- sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, experiment_name = 'test',
                           description = 'this is a test',
                           counts = interaction_counts)
calculateDistances(test, method = 'midpoint')

Description

Get the numbers of interaction types existing in your data

Usage

categoriseInteractions(GIObject, node.classes = NULL, viewpoints = NULL)

Arguments

Value

A data.frame.

Examples

library('GenomicRanges')
data(hic_example_data)
data(mm9_refseq_promoters)
mm9_refseq_grl = split(mm9_refseq_promoters, mm9_refseq_promoters$id)
annotateInteractions(hic_example_data, list(promoter=mm9_refseq_grl))
categoriseInteractions(hic_example_data)

Description

Calculate the number of of paired-end reads mapping between a defined set of anchors. This function will ignore counts present in the input data.

Usage

countsBetweenAnchors(x, y, ...)

## S4 method for signature 'GInteractions,GRanges'
countsBetweenAnchors(x, y, ignore_overlaps = FALSE, ...)

Arguments

Value

A GInteractions object with annotated counts between anchors

Description

Export interactions in BED12 format.

Usage

export.bed12(GIObject, fn = NULL, score = "counts")

## S4 method for signature 'GInteractions'
export.bed12(GIObject, fn = NULL, score = "counts")

Arguments

Value

invisible(1) if outputting to file or a data.frame containing all of the corresponding information

Examples

data(hic_example_data)
export.bed12(hic_example_data, fn = tempfile(), score = 'counts')

Description

#' Exports a GInteractions object to BED-PE format, and writes to a specified file. If filename is not specified, then a data.frame containing the information is returned. The value of the score parameter defines which field is used to populate the score field.

Usage

export.bedpe(GIObject, fn = NULL, score = "counts")

## S4 method for signature 'GInteractions'
export.bedpe(GIObject, fn = NULL, score = "counts")

Arguments

Value

invisible(1) if outputting to file or a data.frame containing all of the corresponding information

Examples

data(hic_example_data)
export.bedpe(hic_example_data, fn = tempfile(), score = 'counts')

Description

Exports a GInteractions object to BEDPE like format, (anchor specifications and a column for reads connecting them) and writes to a specified file. If filename is not specified, then a data.frame containing the information is returned. The value of the score parameter defines which field is used to populate the score field.

Usage

export.chiasig(GIObject, fn = NULL, score = "counts")

## S4 method for signature 'GInteractions'
export.chiasig(GIObject, fn = NULL, score = "counts")

Arguments

Value

invisible(1) if outputting to file or a data.frame containing all of the corresponding information

Examples

data(hic_example_data)
export.chiasig(hic_example_data, fn = tempfile(), score = 'counts')

Description

Exports a GInteractions object to graph.data.frame for use by igraph package. This uses unique anchors as nodes and generates edges between them. For the resulting graph to be easily interpretable, anchors should be non-overlapping. This should already be the case for HiC data (either binned or restriction fragments), however ChIA-PET data can contain overlapping anchors, which may need to be reduced to non-overlapping regions before graph export.

Usage

export.igraph(GIObject)

## S4 method for signature 'GInteractions'
export.igraph(GIObject)

Arguments

Value

a graph.data.frame representation of the GInteractions object

Examples

data(hic_example_data)
ig <- export.igraph(hic_example_data)

Description

Create GenomicInteractions objects from two GRanges ojects.

Usage

GenomicInteractions(anchor1, anchor2, counts, ...)

## S4 method for signature 'GRanges,GRanges,numeric'
GenomicInteractions(anchor1, anchor2, counts, ...)

## S4 method for signature 'GInteractions,ANY,ANY'
GenomicInteractions(anchor1)

## S4 method for signature 'GInteractions,numeric,ANY'
GenomicInteractions(anchor1, anchor2)

## S4 method for signature 'numeric,numeric,GRanges'
GenomicInteractions(anchor1, anchor2, counts, ...)

## S4 method for signature 'GRanges,GRanges,GenomicRanges_OR_missing'
GenomicInteractions(anchor1, anchor2, counts, ...)

## S4 method for signature 'missing,missing,GenomicRanges_OR_missing'
GenomicInteractions(anchor1, anchor2, counts, ...)

## S4 method for signature 'ANY,ANY,ANY'
GenomicInteractions(anchor1, anchor2, counts, ...)

Arguments

Value

a GenomicInteractions object

Examples

library(GenomicRanges)

anchor.one = GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), IRanges(c(10, 20, 30, 20), width=5))
anchor.two = GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), IRanges(c(100, 200, 300, 50), width=5))
interaction_counts = sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, counts=interaction_counts)

Description

@slot metadata List, defaults to 'experiment_name' and 'description', inherited from S4Vectors::Vector @slot anchor_one,anchor_two GRanges. Set of anchors of interactions. @slot counts integer vector, contains raw counts @slot elementMetadata DataFrame

This class is used to store information on which genomic regions are interacting with each other. Objects of this class contain information of the genomic coordinates of the interacting regions and the strength of these interactions, and associated metadata such as the name of the dataset and a brief description of the dataset. Interacting regions are stored as a pair of GenomicRanges: each set of anchor regions is stored as a separate GenomicRanges object, accessed by getAnchorOne and getAnchorTwo.

Examples

showClass('GenomicInteractions')
library(GenomicRanges)

anchor.one = GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), IRanges(c(10, 20, 30, 20), width=5))
anchor.two = GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), IRanges(c(100, 200, 300, 50), width=5))
interaction_counts = sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, counts=interaction_counts)

Description

This function calculates a self ligation threshold according to a method based on that of Heidari et al., Genome Research, 2014. Briefly, paired reads are divided into in evenly spaced bins. For each bin, the number of reads that are aligned to opposite strand vs to the same strand is calculated. A binomial test is used to test if this is significantly different from the 50:50 ratio expected by chance if all reads are real interactions.

Usage

get_binom_ligation_threshold(
  GIObject,
  max.distance = 20000,
  bin.size = 500,
  p.cutoff = 0.05,
  adjust = "fdr",
  plot = TRUE
)

Arguments

Value

The cutoff in base pairs below which an interaction is likely to be a self ligation.

Description

This function calculates a self ligation threshold according to the method published in Heidari et al., Genome Research, 2014. Briefly, paired reads are divided into in evenly sized bins. For each bin, the log2 ratio of reads that are aligned to opposite strand vs to the same strand is calculated. Twice the standard deviation of this ratio at high distances is used a cutoff to determine which bins are likely to contain mostly self-liagted reads.

Usage

get_self_ligation_threshold(
  GIObject,
  bins = 100,
  distance_th = 4e+05,
  plot = TRUE
)

Arguments

Value

The cutoff in base pairs below which an interaction is likely to be a self ligation.

Description

Use these functions to access data stored in each of the slots of a GenomicInteractions object.

Usage

name(GIObject)

anchorOne(GIObject)

anchorTwo(GIObject)

interactionCounts(GIObject)

annotationFeatures(GIObject)

## S4 method for signature 'GInteractions'
name(GIObject)

## S4 method for signature 'GInteractions'
description(object)

## S4 method for signature 'GInteractions'
anchorOne(GIObject)

## S4 method for signature 'GInteractions'
anchorTwo(GIObject)

## S4 method for signature 'GInteractions'
interactionCounts(GIObject)

## S4 method for signature 'GInteractions'
annotationFeatures(GIObject)

Arguments

Value

For 'anchorOne' and 'anchorTwo', a GRanges. For 'interactionCounts', a numeric vector with counts for each interaction in the object. For 'description' and 'name', a character vector with length 1. For 'annotationFeatures', a character vector of features with which the object was previously annotated, or 'NA' if the object is unannotated.

Examples

library(GenomicRanges)

anchor.one = GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), IRanges(c(10, 20, 30, 20), width=5))
anchor.two = GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), IRanges(c(100, 200, 300, 50), width=5))
interaction_counts = sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, counts=interaction_counts)

name(test)
description(test)
anchorOne(test)
anchorTwo(test)
interactionCounts(test)

Description

This dataset contains a subset of the transcripts from the Refseq annotation for mouse genome build hg19 See the ChIA-PET analysis vignette (vignettes(GenomicInteractions)) for more information on how this dataset was created.

Usage

data(hg19.refseq.transcripts)

Format

A GRanges object with length 2441.

A GRanges object

Value

A GRanges object.

Description

This dataset contains HiC data from Seitan et al. 2013. The data was analysed using HOMER (Heinz et al. 2010) at a resolution of 100kb to find significant interactions. This example dataset has been filtered to retain only interactions on chromosomes 14 and 15 with a FDR < 0.1. The data has also been annotated for overlaps with Refseq promoters. See the HiC analysis vignette (vignettes(GenomicInteractions)) for more information on how this dataset was created.

Usage

data(hic_example_data)

Format

A GenomicInteractions object with length 8171.

Value

GenomicInteractions object

References

Seitan, V. C. et al. Cohesin-based chromatin interactions enable regulated gene expression within pre-existing architectural compartments. Genome Res. 23, 2066-77 (2013).

Heinz S, Benner C, Spann N, Bertolino E et al. Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities. Mol Cell 2010 May 28;38(4):576-589.

Description

Create InteractionTrack object from an GenomicInteractions object to visualise a specified chromosome.

Usage

InteractionTrack(x, chromosome = "", name = NULL, start = NULL, end = NULL)

Arguments

Value

an InteractionTrack object

Examples

library(Gviz)

anchor.one <- GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), 
  IRanges(c(10, 20, 30, 20), width=5))
anchor.two <- GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), 
  IRanges(c(100, 200, 300, 50), width=5))
interaction_counts <- sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, experiment_name='test',
                           description='this is a test', counts=interaction_counts)
interactions.track <- InteractionTrack(name='Test', test, chromosome='chr1')
plotTracks(list(interactions.track), chromosome='chr1', from=0, to=500)

Description

@slot giobject GenomicInteractions object from which the object was created

@slot variables list of chromosome, start, and end. Start and end can be NULL

@slot chromosome chromosome defined for the object

@slot stacking character

@slot dp DisplayPars for the object, access with 'availableDisplayPars()'

@slot name Object name

@slot imageMap NULL

Usage

## S4 method for signature 'InteractionTrack'
start(x)

## S4 method for signature 'InteractionTrack'
end(x)

## S4 method for signature 'InteractionTrack'
chromosome(GdObject)

## S4 method for signature 'InteractionTrack'
subset(x, from, to, chromosome, ...)

Arguments

Details

InteractionTrack is a specific Gviz-derived class for enabling the visualisation of chromatin interaction data. The InteractionTrack class allows interactions on a specified chromosome to be visualised by examining interactions between anchors as bezier curves. The object is instantiated and used in a similar fashion to standard Gviz tracks and plotted using the plotTracks.

Several additional display parameters (i.e. displayPars(foo)=list(...) are defined for this class, including plot.anchors which can be used to specify whether anchors are to be drawn. col.anchors.line which can be used to alter the colour of border of these anchor elements and col.anchors.fill can be used to alter the fill colour of these elements.

The value of plot.outside determines whether or not interactions which span outside of the window are to be plotted, and col.outside defines the colour of these interactions. Similarly plot.trans determines whether trans-interactions are plotted and col.trans specifies the colour of trans-interactions.

By default, the height of an arc representing an interaction is proportional to the number of reads/counts supporting that interaction. Instead of using the counts to define this, the height can be set to be proportional to either fdr or p.value using the interaction.measure display parameter. By changing the interaction.dimension to width, the line widths of each arc now represent the statistic supporting them. The heights of the arcs can be made to be proportional to log10 of the supporting statistic by changing interaction.dimension.transform to log.

col.interactions sets the colour of arcs representing interactions within the region of interest. It is possible to colour the arcs by the type of interaction they are involved in (i.e. promoter-promoter interactions etc) by setting the col.interaction.types display parameter to be a named vector of colours, where the name corresponds to the type of interaction. This is applicable to anchors regions through the use of the col.anchors.line.node.class and col.anchors.fill.node.class parameters.

Functions

• start,InteractionTrack-method: Extract stored start position for object or, if that is NULL, minimum of region starts.

• end,InteractionTrack-method: Extract stored end position for object or, if that is NULL, maximum of region ends.

• chromosome,InteractionTrack-method: Extract stored chromosome of object

• subset,InteractionTrack-method: Subset the object to only contain interactions within the specified genomic region

Description

Functions to classify interactions within GInteractions objects.

• 'isInteractionType' takes two character arguments which are annotated node classes and returns interactions between them.

• 'is.pp', 'is.pd' etc. are bindings for common annotations:

  p

  promoter

  d

  distal

  t

  terminator

• 'is.trans' & 'is.cis' select trans-chromosomal and intra-chromosomal interactions, respectively

Usage

is.pp(GIObject)

is.pd(GIObject)

is.pt(GIObject)

is.dd(GIObject)

is.dt(GIObject)

is.tt(GIObject)

isInteractionType(GIObject, x, y)

is.trans(GIObject)

is.cis(GIObject)

## S4 method for signature 'GInteractions'
is.pp(GIObject)

## S4 method for signature 'GInteractions'
is.pd(GIObject)

## S4 method for signature 'GInteractions'
is.pt(GIObject)

## S4 method for signature 'GInteractions'
is.dd(GIObject)

## S4 method for signature 'GInteractions'
is.dt(GIObject)

## S4 method for signature 'GInteractions'
is.tt(GIObject)

## S4 method for signature 'GInteractions'
isInteractionType(GIObject, x, y)

## S4 method for signature 'GInteractions'
is.trans(GIObject)

## S4 method for signature 'GInteractions'
is.cis(GIObject)

Arguments

Value

A logical vector

Examples

data(hic_example_data)
table(is.cis(hic_example_data))
sum(interactionCounts(hic_example_data))

Description

Function to create GenomicInteraction objects from a variety of files. The resulting objects contain information on which genomic regions are interacting with each other, and the number of counts supporting each interaction. It is also possible to store information on associated p-values and false-discovery rates (FDR). It is possible to create GenomicInteractions objects for various datasets including Hi-C and ChIA-PET. It is possible to read interactions from a variety of files including BAM files, bed files (BED12 and BEDPE) and from the output from standard processing pipelines, such as HOMER and ChIA-PET tool. GenomicInteractions objects can also be created using calls of the form new('GenomicInteractions', ...). For hiclib, it expects the directory in which the files extracted using h5dictToTxt.py from the hdf5 file are located, where as for all of the other file types it expects the full filename. Note that recent versions of hiclib (2015-) cannot export the required data and so this function will only work with older files. Hiclib support will be removed in the next version of GenomicInteractions.

Usage

makeGenomicInteractionsFromFile(
  fn,
  type,
  experiment_name = "",
  description = "",
  chr_names = NULL
)

Arguments

Value

a GenomicInteractions object

Examples

k562.rep1 <- makeGenomicInteractionsFromFile(
       system.file(package='GenomicInteractions', 'extdata', 'k562.rep1.cluster.pet3+.txt'),
       type='chiapet.tool', experiment_name='k562', description='k562 pol2 8wg16')

k562.rep1

Description

This dataset contains a subset of the promoters from the Refseq annotation for mouse genome build mm9. See the HiC analysis vignette (vignettes(GenomicInteractions)) for more information on how this dataset was created.

Usage

data(mm9_refseq_promoters)

Format

A GRanges object with length 2441.

A GRanges object

Value

A GRanges object.

Description

Plots summarised coverage of interactions around a set of viewpoints, e.g. promoters. This function requires the output of 'viewPoint()' as input.

Usage

plotAvgViewpoint(
  x,
  left_dist = 1e+05,
  right_dist = 1e+05,
  ylab = "Average signal",
  xlab = "Relative position",
  fix = "center",
  ...
)

Arguments

Value

Coverage that is plotted (invisibly)

Examples

data(hic_example_data)
library(GenomicRanges)
pos <- GRanges(seqnames='chr15', ranges=IRanges(start=59477709, end=59482708))
region <- GRanges(seqnames='chr15', ranges=IRanges(start=58980209, end=59980208))
vp <- viewPoint(hic_example_data, pos, region)
plotAvgViewpoint(vp, left_dist = 1000000, right_dist = 100000)

Description

Plots the percentages of cis and trans interactions for a GInteractions object as a donut plot.

Usage

plotCisTrans(GIObject)

Arguments

Value

A ggplot2 plot

Examples

data(hic_example_data)
plotCisTrans(hic_example_data)

Description

Plot a bar chart of the number of interactions supported by different numbers of reads in your data.

Usage

plotCounts(GIObject, normalise = FALSE, cut = 10)

Arguments

Value

A ggplot2 plot

Examples

data(hic_example_data)
plotCounts(hic_example_data)
plotCounts(hic_example_data, normalise=TRUE)

Description

Plots a histogram of interaction distances for a GInteractions Object

Usage

plotDists(
  GIObject,
  breaks = c(0, 1000, 5000, 10000, 50000, 1e+05, 5e+05, 1e+06, 2e+06),
  method = "midpoint"
)

Arguments

Value

A ggplot2 plot

Examples

data(hic_example_data)
plotDists(hic_example_data)

Description

Plot a donut plot of interaction types for an annotated GInteractions object

Usage

plotInteractionAnnotations(
  GIObject,
  node.classes = NULL,
  viewpoints = NULL,
  other = 0,
  keep.order = FALSE,
  legend = FALSE
)

Arguments

Value

A ggplot2 plot

Examples

library('GenomicRanges')
data(hic_example_data)
data(mm9_refseq_promoters)
mm9_refseq_grl = split(mm9_refseq_promoters, mm9_refseq_promoters$id)
annotateInteractions(hic_example_data, list(promoter=mm9_refseq_grl))
plotInteractionAnnotations(hic_example_data)

Description

Makes summary plots of the counts, interaction distances, interaction annotations, and percentage of cis and trans interactions for a GInteractions object using 'plotCounts', 'plotDists', 'plotCisTrans', and 'plotInteractionAnnotations'.

Usage

plotSummaryStats(GIObject, other = 5, cut = 10)

Arguments

Value

invisible(1)

Examples

data(hic_example_data)
plotSummaryStats(hic_example_data)

Description

Plots coverage of interactions around a given viewpoint. This function requires the output of 'viewPoint()' as input. You should additionally specify the total region you wish to plot.

Usage

plotViewpoint(x, region, ylab = "Signal", xlab = NULL, ...)

Arguments

Value

Coverage that is plotted (invisibly)

Examples

data(hic_example_data)
library(GenomicRanges)
pos <- GRanges(seqnames='chr15', ranges=IRanges(start=59477709, end=59482708))
region <- GRanges(seqnames='chr15', ranges=IRanges(start=58980209, end=59980208))
vp <- viewPoint(hic_example_data, pos, region)
plotViewpoint(vp, region)

Description

Removes all but the first occurence of a duplicated interaction (defined as having identical coordinates for both anchors). N.B. this does not summarise the total counts of all the duplicates. It is designed for removing potential PCR duplicates after reading in .bam files.

Usage

removeDups(GIObject)

Arguments

Value

A GInteractions object that is a subset of the input object.

Description

This function removes all annotations from a GInteractions object by deleting all of the metadata columns associated with both anchors.

Usage

resetAnnotations(GIObject)

## S4 method for signature 'GInteractions'
resetAnnotations(GIObject)

Arguments

Value

invisible(1)

Examples

data(hic_example_data)
resetAnnotations(hic_example_data)

Description

This is designed for processing .bam files.

Usage

sameStrand(GIObject)

Arguments

Value

A logical vector denoting with TRUE if both anchors of an interaction are on the same strand and FALSE otherwise.

Description

Use these functions to set data stored in each of the slots of a GInteractions object.

Usage

name(GIObject) <- value

interactionCounts(GIObject) <- value

## S4 replacement method for signature 'GInteractions'
name(GIObject) <- value

## S4 replacement method for signature 'GInteractions,ANY'
description(object) <- value

## S4 replacement method for signature 'GInteractions'
interactionCounts(GIObject) <- value

Arguments

Value

GenomicInteractions object

Examples

library(GenomicRanges)

anchor.one = GRanges(c('chr1', 'chr1', 'chr1', 'chr1'), IRanges(c(10, 20, 30, 20), width=5))
anchor.two = GRanges(c('chr1', 'chr1', 'chr1', 'chr2'), IRanges(c(100, 200, 300, 50), width=5))
interaction_counts = sample(1:10, 4)
test <- GenomicInteractions(anchor.one, anchor.two, experiment_name='test', 
                           description='this is a test', counts=interaction_counts)

name(test) <- 'Mouse test'
name(test)

description(test) <- 'This is a test using the mouse genome'
description(test)

interactionCounts(test) <- c(2,3,8,5)
interactionCounts(test)

Description

Subsets interactions for which at least one of the anchors overlaps with a given GRanges object. Alternatively, subsets interactions based on annotated feature IDs for a particular feature.

Usage

subsetByFeatures(GIObject, features, feature.class = NULL)

## S4 method for signature 'GInteractions,GRanges,missing'
subsetByFeatures(GIObject, features, feature.class = NULL)

## S4 method for signature 'GInteractions,GRangesList,missing'
subsetByFeatures(GIObject, features, feature.class = NULL)

## S4 method for signature 'GInteractions,character,character'
subsetByFeatures(GIObject, features, feature.class = NULL)

Arguments

Value

a subsetted GInteractions object

Examples

data('hic_example_data')
data('mm9_refseq_promoters')
ids <- names(mm9_refseq_promoters[1:10])
subsetByFeatures(hic_example_data, ids, 'promoter')

Description

Return the total number of interactions in a GInteractions GIObject

Usage

## S4 method for signature 'GInteractions'
sum(x)

Arguments

Value

The sum of the counts in GIObject

Description

This function will calculate the number of observed interactions between two sets of features provided by the end-user. This allows the summarisation of the number of features of a specific type a particular region is involved in and how many interactions exist between them.

Usage

summariseByFeaturePairs(
  GIObject,
  features.one,
  feature.name.one,
  features.two,
  feature.name.two
)

## S4 method for signature 'GInteractions'
summariseByFeaturePairs(
  GIObject,
  features.one,
  feature.name.one,
  features.two,
  feature.name.two
)

Arguments

Value

A data frame with one line for each range in ‘features’

Examples

data('hic_example_data')
data('mm9_refseq_promoters')
data('thymus_enhancers')
annotateInteractions(hic_example_data, list(promoter = mm9_refseq_promoters, enhancer = thymus_enh))
# can be slow so subset of features used for examples
p <- unique(unlist(head(regions(hic_example_data)$promoter.id)))
e <- unique(unlist(head(regions(hic_example_data)$enhancer.id)))
p <- p[!is.na(p)]
p <- mm9_refseq_promoters[p]
e <- e[!is.na(e)]
e <- thymus_enh[e]
ep_summary <- summariseByFeaturePairs(hic_example_data, p, 'promoter', e, 'enhancer')

Description

This function will calculate summary statistics for each element in the given feature set, including the number of interactions (the sum of all interaction counts), number of unique interactions and number of trans- (interchromosomal) interations. It also returns some statistics for the distances of interactions for all interactions of the feature, and for the different interaction types e.g. promoter-distal.

Usage

summariseByFeatures(
  GIObject,
  features,
  feature.name,
  distance.method = "midpoint",
  annotate.self = FALSE
)

## S4 method for signature 'GInteractions'
summariseByFeatures(
  GIObject,
  features,
  feature.name,
  distance.method = "midpoint",
  annotate.self = FALSE
)

Arguments

Value

A data frame with one line for each range in ‘features’

Examples

data('hic_example_data')
data('mm9_refseq_promoters')
annotateInteractions(hic_example_data, list(promoter = mm9_refseq_promoters))
summariseByFeatures(hic_example_data, mm9_refseq_promoters[1:10], 'promoter')

Description

This dataset contains a set of mouse thymus enhancers derived from ChIP-seq data from mouse thymus, as described in Shen et al. 2012. See the HiC analysis vignette for more details. (vignettes(GenomicInteractions)).

Usage

data('thymus_enhancers')

Format

A GRanges object

Value

A GRanges object

References

Shen, Y et al. A map of cis-regulatory sequences in the mouse genome. Nature (2012).

Description

updateObject method for GenomicInteractions 1.3.7 and earlier

Usage

## S4 method for signature 'GenomicInteractions'
updateObject(object, ..., verbose = FALSE)

Arguments

Value

A GenomicInteractions object

Description

This function creates a GInteractions object representing interactions originating at a given viewpoint ('bait'), or set of viewpoints. This is similar to the idea of a virtual 4C experiment where you are interested in interactions with a specific region.

Usage

viewPoint(x, bait, region = NULL, ...)

Arguments

Details

The object returned has the 'bait' as anchor one, and the interacting regions as anchor two. By default this is genome wide. If you only want to consider interactions within a certain distance around the bait, you can specify a region to consider.

Multiple baits can be given, e.g. to find all interactions around promoters.

You may want to visualise the resulting interactions in a genome browser - you can do this by creating coverage over anchor two of the object and exporting as a wig or bedgraph file.

Value

A GInteractions object.

Examples

data(hic_example_data)
library(GenomicRanges)
pos <- GRanges(seqnames='chr15', ranges=IRanges(start=59477709, end=59482708))
region <- GRanges(seqnames='chr15', ranges=IRanges(start=58980209, end=59980208))
vp <- viewPoint(hic_example_data, pos, region)